Cellular proteins form complex networks of interactions with other proteins. Although it is clear that these networks are dynamic structures that change in response to environmental signals and that are altered in disease states, our ability to assess the temporal changes in the networks is still at an early stage. Malaria-infected RBCs are a simple model in which to study changes in protein interaction networks over time. RBCs are highly differentiated cells that lack organelles and that contain a limited set of proteins. During infection, malaria parasites export proteins into the RBC that dramatically alter the properties of the host cell. Because RBCs lack nuclei, and thus cannot respond to parasite infection by altering their protein content, the changes that occur in the RBC phenotype must be due to the approximately 300 malaria proteins that are exported into the RBC. The exported malaria proteins are thought to bind to RBC proteins and, in this way, act as lesions that cause perturbations in the cellular protein interaction network. Since detailed information about the timing of expression of most malaria genes is already available, we can model changes in the cellular protein interaction network over time and correlate those temporal changes in the malaria-RBC interactome with changes in cellular properties. However, functional information about these malaria proteins is scarce and only a very limited number of interactions between exported malaria proteins and RBC proteins have been reported. The overall goal of this research is to elucidate, validate, and temporally model the network of protein-protein interactions between the malaria parasite Plasmodium falciparum and RBC proteins. A combination of new yeast strains, improved yeast two-hybrid screening methodology, and two complementary yeast two-hybrid screening approaches will be used to create a high-quality malaria-RBC protein interaction network. Interactions will be validated by GST pull downs and the split-luciferase assay, which we develop in this project for high-throughput analysis of protein-protein interactions in yeast. From this data we will develop a confidence score for each interaction. The malaria-RBC protein interactions will then be integrated with existing gene expression data to develop a time-dependent view of the malaria-RBC interactome. Using the results of our model as a guide, we will investigate the function of temporally distinct malaria-RBC protein interactions using the resealed RBC ghosts and ektacytometry. This project is innovative in its application and development of technologies to study the malaria-RBC protein interaction network. The data generated from this project is relevant not only to our understanding of interactome dynamics but also for understanding how malaria parasites cause disease.